{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-11-21T09:00:52.373068Z",
     "start_time": "2024-11-21T09:00:52.253335Z"
    }
   },
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Vanilla Transformer data\n",
    "window_sizes_vanilla = [384, 640]\n",
    "train_lengths_vanilla = [384, 640]\n",
    "val_lengths_vanilla = [1024, 1024]\n",
    "train_loss_vanilla = [3.27406, 3.22074]\n",
    "val_loss_vanilla = [5.95275, 4.18709]\n",
    "\n",
    "# Sliding Window data\n",
    "train_lengths_slide = [128, 256, 384, 512, 640, 768, 896, 1024]\n",
    "train_loss_slide = [3.44405, 3.34954, 3.29534, 3.28056, 3.28963, 3.27668, 3.25552, 3.28897]\n",
    "val_loss_slide = [8.06713, 4.8208, 4.41921, 4.09327, 3.29574, 3.28243, 3.26023, 3.28608]\n",
    "\n",
    "# Set min and max values for y-axis scaling\n",
    "y_min = 3.1\n",
    "y_max = 8.5\n",
    "\n",
    "\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Train loss plot\n",
    "ax1.plot(train_lengths_vanilla, train_loss_vanilla, label=\"Vanilla Transformer\", marker='o', color='b')\n",
    "ax1.plot(train_lengths_slide, train_loss_slide, label=\"Sliding Window\", marker='o', color='r')\n",
    "ax1.set_title(\"Train Loss Comparison\")\n",
    "ax1.set_xlabel(\"Train Length\")\n",
    "ax1.set_ylabel(\"Train Loss\")\n",
    "ax1.set_ylim(y_min, y_max)  # Set the same y-axis range for comparison\n",
    "ax1.grid(True)\n",
    "ax1.legend()\n",
    "\n",
    "# Validation loss plot\n",
    "ax2.plot(train_lengths_vanilla, val_loss_vanilla, label=\"Vanilla Transformer\", marker='o', color='b')\n",
    "ax2.plot(train_lengths_slide, val_loss_slide, label=\"Sliding Window\", marker='o', color='r')\n",
    "ax2.set_title(\"Validation Loss Comparison\")\n",
    "ax2.set_xlabel(\"Train Length\")\n",
    "ax2.set_ylabel(\"Validation Loss\")\n",
    "ax2.set_ylim(y_min, y_max)  # Set the same y-axis range for comparison\n",
    "ax2.grid(True)\n",
    "ax2.legend()\n",
    "\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T09:01:50.031819Z",
     "start_time": "2024-11-21T09:01:49.866020Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Updated Sliding Window data\n",
    "train_lengths_slide_updated = [256, 512, 768, 1024, 1280, 1536, 1792, 2048]\n",
    "train_loss_slide_updated = [3.42564, 3.36263, 3.34359, 3.3622, 3.36105, 3.35952, 3.34065, 3.35283]\n",
    "val_loss_slide_updated = [10.78453, 4.84771, 4.40134, 3.50397, 3.35864, 3.3546, 3.34835, 3.35992]\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Set min and max values for y-axis scaling\n",
    "y_min = 3.1\n",
    "y_max = 8.5\n",
    "\n",
    "# Train loss plot\n",
    "ax1.plot(train_lengths_slide_updated, train_loss_slide_updated, label=\"Sliding Window\", marker='o', color='r')\n",
    "ax1.set_title(\"Train Loss for Sliding Window\")\n",
    "ax1.set_xlabel(\"Train Length\")\n",
    "ax1.set_ylabel(\"Train Loss\")\n",
    "ax1.set_ylim(y_min, y_max)  # Set the same y-axis range for comparison\n",
    "ax1.grid(True)\n",
    "ax1.legend()\n",
    "\n",
    "# Validation loss plot\n",
    "ax2.plot(train_lengths_slide_updated, val_loss_slide_updated, label=\"Sliding Window\", marker='o', color='r')\n",
    "ax2.set_title(\"Validation Loss for Sliding Window\")\n",
    "ax2.set_xlabel(\"Train Length\")\n",
    "ax2.set_ylabel(\"Validation Loss\")\n",
    "ax2.set_ylim(y_min, y_max)  # Set the same y-axis range for comparison\n",
    "ax2.grid(True)\n",
    "ax2.legend()\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "86ca3d83f083c04c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-27T07:38:33.670486Z",
     "start_time": "2024-11-27T07:38:33.517589Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# window size = 512\n",
    "\n",
    "# | Window Size | Layers | Train Length | Val Length | Train Loss | Val Loss |\n",
    "# | ----------- | ------ | ------------ | ---------- | ---------- | -------- |\n",
    "# | 512         | 8      | 3072         | 32768      |            |          |\n",
    "# | 512         | 12     | 3072         | 32768      |            |          |\n",
    "# | 512         | 16     | 3072         | 32768      | 3.27698    | 3.24785  |\n",
    "# | 512         | 24     | 3072         | 32768      | 3.20554    | 3.44778  |\n",
    "# | 512         | 36     | 3072         | 32768      | 3.14282    | 3.46975  |\n",
    "\n",
    "# Data for Train: 3072; Val: 32768\n",
    "layers_768 = [16, 24, 36]\n",
    "train_loss_768 = [3.27698, 3.20554, 3.14282]\n",
    "val_loss_768 = [3.24785, 3.44778, 3.46975]\n",
    "\n",
    "\n",
    "# | Window Size | Layers | Train Length | Val Length | Train Loss | Val Loss |\n",
    "# | ----------- | ------ | ------------ | ---------- | ---------- | -------- |\n",
    "# | 512         | 8      | 2560         | 32768      | 3.43962    | 3.47338  |\n",
    "# | 512         | 12     | 2560         | 32768      | 3.33871    | 3.42149  |\n",
    "# | 512         | 16     | 2560         | 32768      | 3.27799    | 3.48548  |\n",
    "# | 512         | 24     | 2560         | 32768      | 3.20384    | 3.82643  |\n",
    "# | 512         | 36     | 2560         | 32768      | 3.14162    | 3.79894  |\n",
    "\n",
    "\n",
    "# Data for Train: 2560; Val: 32768\n",
    "layers_1280 = [8, 12, 16, 24, 36]\n",
    "train_loss_1280 = [3.43962, 3.33871, 3.27799, 3.20384, 3.14162]\n",
    "val_loss_1280 = [3.47338, 3.42149, 3.48548, 3.82643, 3.79894]\n",
    "\n",
    "# Data for Train: 2048; Val: 32768\n",
    "layers_1024 = [36]\n",
    "train_loss_1024 = [3.14535]\n",
    "val_loss_1024 = [5.4548]\n",
    "\n",
    "# Determine global min and max for y-axis across both train and validation loss\n",
    "y_min, y_max = 3, 5.5\n",
    "\n",
    "# Create subplots with updated y-axis limits\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Train loss plot\n",
    "ax1.plot(layers_768, train_loss_768, label=\"Train Length: 3072 (x6)\", marker='s', color='y')\n",
    "ax1.plot(layers_1280, train_loss_1280, label=\"Train Length: 2560 (x5)\", marker='o', color='b')\n",
    "ax1.plot(layers_1024, train_loss_1024, label=\"Train Length: 2048 (x4)\", marker='o', color='r')\n",
    "ax1.set_title(\"Train Loss vs Layers\")\n",
    "ax1.set_xlabel(\"Number of Layers\")\n",
    "ax1.set_ylabel(\"Train Loss\")\n",
    "ax1.legend()\n",
    "ax1.grid(True)\n",
    "ax1.set_ylim(y_min, y_max)\n",
    "ax1.set_xlim(0, 90)\n",
    "\n",
    "# Validation loss plot\n",
    "ax2.plot(layers_768, val_loss_768, label=\"Train Length: 3072 (x6)\", marker='s', color='y')\n",
    "ax2.plot(layers_1280, val_loss_1280, label=\"Train Length: 2560 (x5)\", marker='o', color='b')\n",
    "ax2.plot(layers_1024, val_loss_1024, label=\"Train Length: 2048 (x4)\", marker='o', color='r')\n",
    "ax2.set_title(\"Validation Loss vs Layers\")\n",
    "ax2.set_xlabel(\"Number of Layers\")\n",
    "ax2.set_ylabel(\"Validation Loss\")\n",
    "ax2.legend()\n",
    "ax2.grid(True)\n",
    "ax2.set_ylim(y_min, y_max)\n",
    "ax2.set_xlim(0, 90)\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "96879f729f24144b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-27T07:38:35.248375Z",
     "start_time": "2024-11-27T07:38:35.089983Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for Train: 1280; Val: 2048\n",
    "layers_1280 = [10, 12, 18, 20, 24, 36, 40, 44, 48]\n",
    "train_loss_1280 = [3.38644, 3.36105, 3.27066, 3.26174, 3.2214, 3.16794, 3.14309, 3.14137, 3.13797]\n",
    "val_loss_1280 = [3.39689, 3.35864, 3.3098, 3.29119, 3.25807, 3.22473, 3.20571, 3.21288, 3.41579]\n",
    "\n",
    "# Data for Train: 1024; Val: 2048\n",
    "layers_1024 = [4, 6, 8, 10, 12, 13, 14, 16, 18]\n",
    "train_loss_1024 = [3.67824, 3.53066, 3.449, 3.38925, 3.3622, 3.33116, 3.3315, 3.28525, 3.26763]\n",
    "val_loss_1024 = [3.66411, 3.53088, 3.49393, 3.48962, 3.50397, 3.58842, 3.87566, 3.63399, 3.67015]\n",
    "\n",
    "# Data for Train: 768; Val: 2048\n",
    "layers_768 = [2, 3, 4, 5, 6, 7, 8, 10, 12]\n",
    "train_loss_768 = [4.10489, 3.79363, 3.70949, 3.59408, 3.54206, 3.48148, 3.42859, 3.38145, 3.34293]\n",
    "val_loss_768 = [4.08787, 3.77111, 3.73329, 3.8204, 4.22443, 4.26603, 4.25381, 4.34246, 4.41327]\n",
    "\n",
    "# Determine global min and max for y-axis across both train and validation loss\n",
    "y_min, y_max = 3, 5.5\n",
    "\n",
    "# Create subplots with updated y-axis limits\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Train loss plot\n",
    "ax1.plot(layers_1280, train_loss_1280, label=\"Train Length: 1280 (x5)\", marker='o', color='b')\n",
    "ax1.plot(layers_1024, train_loss_1024, label=\"Train Length: 1024 (x4)\", marker='o', color='r')\n",
    "ax1.plot(layers_768, train_loss_768, label=\"Train Length: 768 (x3)\", marker='o', color='g')\n",
    "ax1.set_title(\"Train Loss vs Layers\")\n",
    "ax1.set_xlabel(\"Number of Layers\")\n",
    "ax1.set_ylabel(\"Train Loss\")\n",
    "ax1.legend()\n",
    "ax1.grid(True)\n",
    "ax1.set_ylim(y_min, y_max)\n",
    "ax1.set_xlim(0, 90)\n",
    "\n",
    "# Validation loss plot\n",
    "ax2.plot(layers_1280, val_loss_1280, label=\"Train Length: 1280 (x5)\", marker='o', color='b')\n",
    "ax2.plot(layers_1024, val_loss_1024, label=\"Train Length: 1024 (x4)\", marker='o', color='r')\n",
    "ax2.plot(layers_768, val_loss_768, label=\"Train Length: 768 (x3)\", marker='o', color='g')\n",
    "ax2.set_title(\"Validation Loss vs Layers\")\n",
    "ax2.set_xlabel(\"Number of Layers\")\n",
    "ax2.set_ylabel(\"Validation Loss\")\n",
    "ax2.legend()\n",
    "ax2.grid(True)\n",
    "ax2.set_ylim(y_min, y_max)\n",
    "ax2.set_xlim(0, 90)\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "2de307f90be6c5e3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-27T07:38:36.080078Z",
     "start_time": "2024-11-27T07:38:35.922927Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Data for Train: 1280; Val: 2048\n",
    "layers_1280 = [10, 12, 18, 20, 24, 36, 40, 44, 48]\n",
    "train_loss_1280 = [3.38644, 3.36105, 3.27066, 3.26174, 3.2214, 3.16794, 3.14309, 3.14137, 3.13797]\n",
    "val_loss_1280 = [3.39689, 3.35864, 3.3098, 3.29119, 3.25807, 3.22473, 3.20571, 3.21288, 3.41579]\n",
    "\n",
    "# Data for Train: 1024; Val: 2048\n",
    "layers_1024 = [4, 6, 8, 10, 12, 13, 14, 16, 18]\n",
    "train_loss_1024 = [3.67824, 3.53066, 3.449, 3.38925, 3.3622, 3.33116, 3.3315, 3.28525, 3.26763]\n",
    "val_loss_1024 = [3.66411, 3.53088, 3.49393, 3.48962, 3.50397, 3.58842, 3.87566, 3.63399, 3.67015]\n",
    "\n",
    "# Data for Train: 768; Val: 2048\n",
    "layers_768 = [2, 3, 4, 5, 6, 7, 8, 10, 12]\n",
    "train_loss_768 = [4.10489, 3.79363, 3.70949, 3.59408, 3.54206, 3.48148, 3.42859, 3.38145, 3.34293]\n",
    "val_loss_768 = [4.08787, 3.77111, 3.73329, 3.8204, 4.22443, 4.26603, 4.25381, 4.34246, 4.41327]\n",
    "\n",
    "# Determine global min and max for y-axis across both train and validation loss\n",
    "y_min, y_max = 3, 5.5\n",
    "\n",
    "# Find minimum validation loss points\n",
    "min_val_1280_idx = np.argmin(val_loss_1280)\n",
    "min_val_1024_idx = np.argmin(val_loss_1024)\n",
    "min_val_768_idx = np.argmin(val_loss_768)\n",
    "\n",
    "min_points = [\n",
    "    (layers_1280[min_val_1280_idx], val_loss_1280[min_val_1280_idx]),\n",
    "    (layers_1024[min_val_1024_idx], val_loss_1024[min_val_1024_idx]),\n",
    "    (layers_768[min_val_768_idx], val_loss_768[min_val_768_idx]),\n",
    "]\n",
    "\n",
    "# Fit a curve through these points\n",
    "x_min_points = [point[0] for point in min_points]\n",
    "y_min_points = [point[1] for point in min_points]\n",
    "\n",
    "# Use a polynomial fit (degree=2 for simplicity)\n",
    "poly_coeffs = np.polyfit(x_min_points, y_min_points, 2)\n",
    "poly_fit = np.poly1d(poly_coeffs)\n",
    "\n",
    "# Generate x values for the curve\n",
    "x_fit = np.linspace(min(x_min_points), max(x_min_points), 100)\n",
    "y_fit = poly_fit(x_fit)\n",
    "\n",
    "# Create subplots with updated y-axis limits\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Train loss plot\n",
    "ax1.plot(layers_1280, train_loss_1280, label=\"Train Length: 1280 (x5)\", marker='o', color='b')\n",
    "ax1.plot(layers_1024, train_loss_1024, label=\"Train Length: 1024 (x4)\", marker='o', color='r')\n",
    "ax1.plot(layers_768, train_loss_768, label=\"Train Length: 768 (x3)\", marker='o', color='g')\n",
    "ax1.set_title(\"Train Loss vs Layers\")\n",
    "ax1.set_xlabel(\"Number of Layers\")\n",
    "ax1.set_ylabel(\"Train Loss\")\n",
    "ax1.legend()\n",
    "ax1.grid(True)\n",
    "ax1.set_ylim(y_min, y_max)\n",
    "ax1.set_xlim(0, 90)\n",
    "\n",
    "# Validation loss plot\n",
    "ax2.plot(layers_1280, val_loss_1280, label=\"Train Length: 1280 (x5)\", marker='o', color='b')\n",
    "ax2.plot(layers_1024, val_loss_1024, label=\"Train Length: 1024 (x4)\", marker='o', color='r')\n",
    "ax2.plot(layers_768, val_loss_768, label=\"Train Length: 768 (x3)\", marker='o', color='g')\n",
    "ax2.scatter(x_min_points, y_min_points, color='orange', label=\"Min Val Points\", zorder=5)\n",
    "ax2.plot(x_fit, y_fit, color='purple', linestyle='--', label=\"Fitted Curve\")\n",
    "ax2.set_title(\"Validation Loss vs Layers\")\n",
    "ax2.set_xlabel(\"Number of Layers\")\n",
    "ax2.set_ylabel(\"Validation Loss\")\n",
    "ax2.legend()\n",
    "ax2.grid(True)\n",
    "ax2.set_ylim(y_min, y_max)\n",
    "ax2.set_xlim(0, 90)\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "e2765d6f1a2d907d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-27T07:38:36.432377Z",
     "start_time": "2024-11-27T07:38:36.256711Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for Train: 1280; Val: 2048\n",
    "layers_1280 = [10, 12, 18, 20, 24, 36, 40, 44, 48]\n",
    "train_loss_1280 = [3.38644, 3.36105, 3.27066, 3.26174, 3.2214, 3.16794, 3.14309, 3.14137, 3.13797]\n",
    "val_loss_1280 = [3.39689, 3.35864, 3.3098, 3.29119, 3.25807, 3.22473, 3.20571, 3.21288, 3.41579]\n",
    "\n",
    "# Data for Train: 1024; Val: 2048\n",
    "layers_1024 = [4, 6, 8, 10, 12, 13, 14, 16, 18]\n",
    "train_loss_1024 = [3.67824, 3.53066, 3.449, 3.38925, 3.3622, 3.33116, 3.3315, 3.28525, 3.26763]\n",
    "val_loss_1024 = [3.66411, 3.53088, 3.49393, 3.48962, 3.50397, 3.58842, 3.87566, 3.63399, 3.67015]\n",
    "\n",
    "# Data for Train: 768; Val: 2048\n",
    "layers_768 = [2, 3, 4, 5, 6, 7, 8, 10, 12]\n",
    "train_loss_768 = [4.10489, 3.79363, 3.70949, 3.59408, 3.54206, 3.48148, 3.42859, 3.38145, 3.34293]\n",
    "val_loss_768 = [4.08787, 3.77111, 3.73329, 3.8204, 4.22443, 4.26603, 4.25381, 4.34246, 4.41327]\n",
    "\n",
    "# Determine global min and max for y-axis across both train and validation loss\n",
    "y_min, y_max = 3, 5.5\n",
    "\n",
    "# Create a single plot with both train and validation loss\n",
    "plt.figure(figsize=(20, 12))\n",
    "\n",
    "# Train loss plots\n",
    "plt.plot(layers_1280, train_loss_1280, label=\"Train (Length: 1280)\", marker='o', linestyle='-', color='b')\n",
    "plt.plot(layers_1024, train_loss_1024, label=\"Train (Length: 1024)\", marker='o', linestyle='-', color='r')\n",
    "plt.plot(layers_768, train_loss_768, label=\"Train (Length: 768)\", marker='o', linestyle='-', color='g')\n",
    "\n",
    "# Validation loss plots\n",
    "plt.plot(layers_1280, val_loss_1280, label=\"Validation (Length: 1280)\", marker='s', linestyle='--', color='b')\n",
    "plt.plot(layers_1024, val_loss_1024, label=\"Validation (Length: 1024)\", marker='s', linestyle='--', color='r')\n",
    "plt.plot(layers_768, val_loss_768, label=\"Validation (Length: 768)\", marker='s', linestyle='--', color='g')\n",
    "\n",
    "# Labels, title, and legend\n",
    "plt.title(\"Train and Validation Loss vs Layers\")\n",
    "plt.xlabel(\"Number of Layers\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xlim(0, 90)\n",
    "\n",
    "# Display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "b5589648ee51a0cb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-27T07:38:36.706304Z",
     "start_time": "2024-11-27T07:38:36.568951Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for Train: 768; Val: 1024\n",
    "layers_640 = [36, 72]\n",
    "train_loss_640 = [3.27913, 3.20924, 3.21099, 3.18473, 3.16777, 3.1536, 3.14092, 3.13749]\n",
    "val_loss_640 = [3.29857, 3.2314, 3.2457, 3.21908, 3.21377, 3.17603, 3.1743, 3.34921]\n",
    "\n",
    "# Data for Train: 640; Val: 1024\n",
    "layers_640 = [24, 36, 42, 48, 60, 66, 72, 84]\n",
    "train_loss_640 = [3.27913, 3.20924, 3.21099, 3.18473, 3.16777, 3.1536, 3.14092, 3.13749]\n",
    "val_loss_640 = [3.29857, 3.2314, 3.2457, 3.21908, 3.21377, 3.17603, 3.1743, 3.34921]\n",
    "\n",
    "# Data for Train: 512; Val: 1024\n",
    "layers_512 = [10, 12, 14]\n",
    "train_loss_512 = [3.43861, 3.40522, 3.37066]\n",
    "val_loss_512 = [3.50487, 3.50113, 3.66947]\n",
    "\n",
    "# Plot with consistent y-axis limits\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Train loss plot\n",
    "ax1.plot(layers_640, train_loss_640, label=\"Train Length: 640 (x5)\", marker='o', color='b')\n",
    "ax1.plot(layers_512, train_loss_512, label=\"Train Length: 512 (x4)\", marker='o', color='r')\n",
    "ax1.set_title(\"Train Loss vs Layers\")\n",
    "ax1.set_xlabel(\"Number of Layers\")\n",
    "ax1.set_ylabel(\"Train Loss\")\n",
    "ax1.legend()\n",
    "ax1.grid(True)\n",
    "ax1.set_ylim(y_min, y_max)\n",
    "ax1.set_xlim(0, 90)\n",
    "\n",
    "# Validation loss plot\n",
    "ax2.plot(layers_640, val_loss_640, label=\"Train Length: 640 (x5)\", marker='o', color='b')\n",
    "ax2.plot(layers_512, val_loss_512, label=\"Train Length: 512 (x4)\", marker='o', color='r')\n",
    "ax2.set_title(\"Validation Loss vs Layers\")\n",
    "ax2.set_xlabel(\"Number of Layers\")\n",
    "ax2.set_ylabel(\"Validation Loss\")\n",
    "ax2.legend()\n",
    "ax2.grid(True)\n",
    "ax2.set_ylim(y_min, y_max)\n",
    "ax2.set_xlim(0, 90)\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "60f1175153b7d399",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "52c8f97f9ec29615"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
